FFHQ 的总训练时间为 9 天，LSUN CAR 为 13 天。整个项目，包括所有探索，消耗了 132 兆瓦时的电力...
/nvidia/research/stylegan2/versions/1/files/stylegan2-afhqcat-512x512.pkl wget http://d36zk2xti64re0.../stylegan2/networks/stylegan2-cat-config-f.pkl 运行DragGAN服务 执行以下命令运行DragGAN服务,拖拽即可调整物体姿态,具体使用详见官方演示。...这允许进行实时的交互式编辑会话,用户可以在其中快速...
│ ├ mydata-512x512.tfr [example] prepared dataset │ └ ⋯ ├ models trained networks for inference/generation │ └ ffhq-1024.pkl [example] trained network file (may contain Gs only) ├ src source code └ train training folders ├ ffhq-512.pkl [example] pre-trained model...
├ ffhq.pkl FFHQ at 1024x1024, trained using original StyleGAN2 ├ metfaces.pkl MetFaces at 1024x1024, transfer learning from FFHQ using ADA ├ afhqcat.pkl AFHQ Cat at 512x512, trained from scratch using ADA ├ afhqdog.pkl AFHQ...
1)支持非正方形图像,如:768x512或640x384等。 2)支持垂直镜像增强。 3)支持自动从最新的 pkl 中继续训练。 4)只创建最大的 tfrecord;,使用原始 JPEG 代替解码 numpy 数组,大大减少 tfrecord 创建时间和数据集大小。 这个项目主要的计算工作是在通过对总统图像的映射以获得隐状态。这会消耗大约10分钟/图,这...
'ffhq512': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/ffhq-res512-mirror-stylegan2-noaug.pkl', 'ffhq1024': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/transfer-learning-source-nets/ffhq-res1024-mirror-stylegan2-noaug.pkl', ...
The results of each training run are saved to a newly created directory, for example ~/training-runs/00000-stylegan3-t-afhqv2-512x512-gpus8-batch32-gamma8.2. The training loop exports network pickles (network-snapshot-.pkl) and random image grids (fakes.png) at regular intervals (controlle...
python generate.py --outdir=out --projected_w=out/projected_w.npz \ --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl Using networks from PythonYou can use pre-trained networks in your own Python code as follows:...
stylegan2-ffhq-config-f.pkl野区**叔叔 上传363.99MB 文件格式 pkl stylegan2 stylegan2 ffhq config 点赞(0) 踩踩(0) 反馈 所需:1 积分 电信网络下载 AI基础知识图文教程 --入门知识学习.docx 2025-03-24 15:25:49 积分:1 AI工程师岗位毕业生薪酬是多少?AI就业前景如何?.docx 2025-03-24 15:23...
This model is backed by NVIDIA’s Plus Plus (++) Promise Click hereto learn more about the quality of the datasets used to train this model FieldResponse Participation considerations from adversely impacted groups(protected classes)in model design and testing:Age, Race (Flickr-Faces-HQ (FFHQ)) ...